Inferring Metabolic Flux from Time-Course Metabolomics

Productivity enhancement in L-lysine fermentation using oxygen-enhanced bioreactor and oxygen vector

Introduction: L-lysine is a bulk product. In industrial production using high-biomass fermentation, the high density of bacteria and the intensity of production require sufficient cellular respiratory metabolism for support. Conventional bioreactors often have difficulty meeting the oxygen supply conditions for this fermentation process, which is not conducive to improving the sugar-amino acid conversion rate. In this study, we designed and developed an oxygen-enhanced bioreactor to address this problem. Methods: This bioreactor optimizes the aeration mix using an internal liquid flow guide and multiple propellers. Results: Compared with a conventional bioreactor, it improved the k_La from 367.57 to 875.64 h^-1, an increase of 238.22%. The results show that the oxygen supply capacity of the oxygen-enhanced bioreactor is better than that of the conventional bioreactor. Its oxygenating effect increased the dissolved oxygen in the middle and late stages of fermentation by an average of 20%. The increased viability of Corynebacterium glutamicum LS260 in the mid to late stages of growth resulted in a yield of 185.3 g/L of L-lysine, 74.57% conversion of lysine from glucose, and productivity of 2.57 g/L/h, an increase of 11.0%, 6.01%, and 8.2%, respectively, over a conventional bioreactor. Oxygen vectors can further improve the production performance of lysine strains by increasing the oxygen uptake capacity of microorganisms. We compared the effects of different oxygen vectors on the production of L-lysine from LS260 fermentation and concluded that n-dodecane was the most suitable. Bacterial growth was smoother under these conditions, with a 2.78% increase in bacterial volume, a 6.53% increase in lysine production, and a 5.83% increase in conversion. The different addition times of the oxygen vectors also affected the final yield and conversion, with the addition of oxygen vectors at 0 h, 8 h, 16 h, and 24 h of fermentation increasing the yield by 6.31%, 12.44%, 9.93%, and 7.39%, respectively, compared to fermentation without the addition of oxygen vectors. The conversion rates increased by 5.83%, 8.73%, 7.13%, and 6.13%, respectively. The best results were achieved by adding oxygen vehicles at the 8th hour of fermentation, with a lysine yield of 208.36 g/L and a conversion rate of 83.3%. In addition, n-dodecane significantly reduced the amount of foam produced during fermentation, which is beneficial for fermentation control and equipment. Conclusion: The new oxygen-enhanced bioreactor improves oxygen transfer efficiency, and oxygen vectors enhance the ability of cells to take up oxygen, which effectively solves the problem of insufficient oxygen supply during lysine fermentation. This study provides a new bioreactor and production solution for lysine fermentation.

Protocol for CAROM: A machine learning tool to predict post-translational regulation from metabolic signatures

This protocol describes CAROM, a computational tool that combines genome-scale metabolic networks (GEMs) and machine learning to identify enzyme targets of post-translational modifications (PTMs). Condition-specific enzyme and reaction properties are used to predict targets of phosphorylation and acetylation in multiple organisms. CAROM is influenced by the accuracy of GEMs and associated flux-balance analysis (FBA), which generate the inputs of the model. We demonstrate the protocol using multi-omics data from E. coli. For complete details on the use and execution of this protocol, please refer to Smith et al. (2022).

Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease

Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.

Metabolic signatures of regulation by phosphorylation and acetylation

Acetylation and phosphorylation are highly conserved posttranslational modifications (PTMs) that regulate cellular metabolism, yet how metabolic control is shared between these PTMs is unknown. Here we analyze transcriptome, proteome, acetylome, and phosphoproteome datasets in E. coli, S. cerevisiae, and mammalian cells across diverse conditions using CAROM, a new approach that uses genome-scale metabolic networks and machine learning to classify targets of PTMs. We built a single machine learning model that predicted targets of each PTM in a condition across all three organisms based on reaction attributes (AUC>0.8). Our model predicted phosphorylated enzymes during a mammalian cell-cycle, which we validate using phosphoproteomics. Interpreting the machine learning model using game theory uncovered enzyme properties including network connectivity, essentiality, and condition-specific factors such as maximum flux that differentiate targets of phosphorylation from acetylation. The conserved and predictable partitioning of metabolic regulation identified here between these PTMs may enable rational rewiring of regulatory circuits.

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.

Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling

Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications. This metabolism-epigenome cross talk plays a fundamental role in development, immune function, and diseases like cancer. Yet, major challenges remain in understanding the interactions between cellular metabolism and the epigenome. How the levels and fluxes of various metabolites impact epigenetic marks is still unclear. Discussed herein are recent applications and the potential of systems biology methods such as flux tracing and metabolic modeling to address these challenges and to uncover new metabolic-epigenetic interactions. These systems approaches can ultimately help elucidate how nutrients shape the epigenome of microbes and mammalian cells.